U.S. patent application number 16/853421 was filed with the patent office on 2020-08-06 for systems and methods for detecting worsening heart failure.
The applicant listed for this patent is Cardiac Pacemakers, Inc.. Invention is credited to Qi An, Viktoria A. Averina, Pramodsingh Hirasingh Thakur, Yi Zhang.
Application Number | 20200245951 16/853421 |
Document ID | 20200245951 / US20200245951 |
Family ID | 1000004767854 |
Filed Date | 2020-08-06 |
Patent Application | download [pdf] |
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United States Patent
Application |
20200245951 |
Kind Code |
A1 |
Thakur; Pramodsingh Hirasingh ;
et al. |
August 6, 2020 |
SYSTEMS AND METHODS FOR DETECTING WORSENING HEART FAILURE
Abstract
Systems and methods for detecting worsening cardiac conditions
such as worsening heart failure events are described. A system may
include sensor circuits to sense physiological signals and signal
processors to generate from the physiological signals first and
second signal metrics. The system may include a risk stratifier
circuit to produce a cardiac risk indication. The system may use at
least the first signal metric to generate a primary detection
indication, and use at least the second signal metric and the risk
indication to generate a secondary detection indication. The risk
indication may be used to modulate the second signal metric. A
detector circuit may detect the worsening cardiac event using the
primary and secondary detection indications.
Inventors: |
Thakur; Pramodsingh Hirasingh;
(Woodbury, MN) ; Zhang; Yi; (Plymouth, MN)
; An; Qi; (Blaine, MN) ; Averina; Viktoria A.;
(Shoreview, MN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Cardiac Pacemakers, Inc. |
St. Paul |
MN |
US |
|
|
Family ID: |
1000004767854 |
Appl. No.: |
16/853421 |
Filed: |
April 20, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15473783 |
Mar 30, 2017 |
10660577 |
|
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16853421 |
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62316905 |
Apr 1, 2016 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61N 1/3627 20130101;
A61B 5/0816 20130101; A61B 5/091 20130101; A61B 5/0205 20130101;
A61B 5/7282 20130101; A61B 5/7264 20130101; A61N 1/36585 20130101;
A61B 5/7275 20130101; G16H 40/63 20180101; A61B 5/4836 20130101;
G16H 50/30 20180101; A61B 5/746 20130101; A61B 7/04 20130101; A61B
5/686 20130101; G16H 50/20 20180101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; G16H 50/20 20060101 G16H050/20; G16H 50/30 20060101
G16H050/30; G16H 40/63 20060101 G16H040/63; A61B 5/091 20060101
A61B005/091; A61N 1/365 20060101 A61N001/365; A61N 1/362 20060101
A61N001/362; A61B 5/0205 20060101 A61B005/0205 |
Claims
1. A medical system for detecting a disease status, the medical
system comprising: a signal processor circuit configured to:
generate a first signal metric based on a first physiological
signal received from a patient; and generate a second signal
metric, different from the first signal metric, from a second
physiological signal received from the patient; a detector circuit
configured to: receive or determine a risk indication indicating a
risk of the patient; generate a primary detection indication based
on the first signal metric; generate a secondary detection
indication, different from the primary detection indication, based
on the second signal metric and the risk indication; and determine
the disease status using a combination of the primary detection
indication and the secondary detection indication; and an output
circuit configured to generate an alert based on the determined
disease status.
2. The medical system of claim 1, wherein the detector circuit is
configured to determine the disease status including worsening of
at least one of a cardiac condition, a pulmonary condition, or a
renal condition.
3. The medical system of claim 1, wherein the first and second
physiological signals each include at least one of: a heart sound
signal; a thoracic or cardiac impedance signal; a reparation
signal; a physical activity signal; or a blood pressure signal.
4. The medical system of claim 1, wherein the detector circuit is
configured to determine the disease status using a Boolean-logic or
fuzzy-logic combination of the primary detection indication and the
secondary detection indication.
5. The medical system of claim 1, wherein the primary detection
indication includes a primary detection score representing a trend
of the first signal metric over time, and wherein the secondary
detection indication includes a secondary detection score
representing a trend of the second signal metric over time
modulated by the risk indication.
6. The medical system of claim 5, wherein the detector circuit is
configured to generate the secondary detection score using a
product of the trend of the second signal metric and the risk
indication.
7. The medical system of claim 5, wherein the detector circuit is
configured to generate the secondary detection score using samples
taken from the trend of the second signal metric in response to the
risk indication satisfying a condition.
8. The medical system of claim 1, wherein: the signal processor
circuit is configured to generate a third signal metric from a
third physiological signal, the third signal metric being different
from the first and second signal metrics; and the detector circuit
is configured to: generate a third detection indication based on
third signal metric, the third detection indication being different
from the primary and secondary detection indications; and determine
the disease status using a combination of the primary detection
indication and the third detection indication if the secondary
detection indication indicates no detection of a worsened disease
status.
9. The medical system of claim 1, wherein the detector circuit is
configured to: determine a primary risk indication from the first
physiological signal; determine a secondary risk indication from
the second physiological signal; and determine the risk indication
using a combination of the primary and secondary risk
indications.
10. The medical system of claim 9, wherein the detector circuit is
configured to determine the secondary risk indication using
measurements from the second physiological signal when the third
physiological signal satisfies a condition, the third physiological
signal sensed from the patient and different from the first and
second physiological signals.
11. The medical system of claim 9, wherein the risk stratifier
circuit is configured to transform the composite cardiac risk
indication into numerical values with a specific range.
12. The medical system of claim 11, wherein the risk stratifier
circuit is configured to transform the composite cardiac risk
indication using a sigmoid function.
13. The medical system of claim 1, comprising a therapy circuit
configured to deliver a therapy in response to the determination of
a worsened disease status.
14. A method comprising: generating, via a signal processor
circuit, a first signal metric from a first physiological signal
sensed from a patient; generating, via the signal processor
circuit, a second signal metric, different from the first signal
metric, from a second physiological signal sensed from the patient;
receiving or determining, via a detector circuit, a risk indication
indicating a risk of the patient; generating, via the detector
circuit, a primary detection indication based on the first signal
metric; generating, via the detector circuit, a secondary detection
indication, different from the primary detection indication, based
on the second signal metric and the risk indication; determining
the disease status using a combination of the primary detection
indication and the secondary detection indication; and generating,
via an output circuit, an alert based on the determined disease
status.
15. The method of claim 14, wherein the first and second
physiological signals each include at least one of: a heart sound
signal; a thoracic or cardiac impedance signal; a reparation
signal; a physical activity signal; or a blood pressure signal.
16. The method of claim 14, wherein determining the disease status
includes using a Boolean-logic or fuzzy-logic combination of the
primary detection indication and the secondary detection
indication.
17. The method of claim 14, wherein the primary detection
indication includes a primary detection score representing a trend
of the first signal metric over time, and wherein the secondary
detection indication includes a secondary detection score
representing a trend of the second signal metric over time
modulated by the risk indication.
18. The method of claim 14, comprising: generating a third signal
metric from a third physiological signal, the third signal metric
being different from the first and second signal metrics;
generating a third detection indication based on third signal
metric, the third detection indication being different from the
primary and secondary detection indications; and determining the
disease status using a combination of the primary detection
indication and the third detection indication if the secondary
detection indication indicates no detection of a worsened disease
status.
19. The method of claim 14, wherein determining the risk indication
includes: determining a primary risk indication from the first
physiological signal; determining a secondary risk indication from
the second physiological signal; and determining the risk
indication using a combination of the primary and secondary risk
indications.
20. The method of claim 19, comprising: receiving a third
physiological signal sensed from the patient and different from the
first and second physiological signals; and determining the
secondary risk indication using measurements from the second
physiological signal when the third physiological signal satisfies
a condition.
Description
CLAIM OF PRIORITY
[0001] This application is a continuation of U.S. application Ser.
No. 15/473,783, filed Mar. 30, 2027, which claims the benefit of
priority under 35 U.S.C. .sctn. 119(e) of U.S. Provisional Patent
Application Ser. No. 62/316,905, filed on Apr. 1, 2016, which is
herein incorporated by reference in its entirety.
TECHNICAL FIELD
[0002] This document relates generally to medical devices, and more
particularly, to systems, devices and methods for detecting and
monitoring events indicative of worsening of congestive heart
failure.
BACKGROUND
[0003] Congestive heart failure (CHF or HF) is a major health
problem and affects many people in the United States alone. CHF
patients may have enlarged heart with weakened cardiac muscles,
resulting in poor cardiac output of blood. Although CHF is usually
a chronic condition, it may occur suddenly. It may affect the left
heart, right heart or both sides of the heart. If CHF affects the
left ventricle, signals that control the left ventricular
contraction are delayed, and the left and right ventricles do not
contract simultaneously. Non-simultaneous contractions of the left
and right ventricles further decrease the pumping efficiency of the
heart.
[0004] In many CHF patients, elevated pulmonary vascular pressures
may cause fluid accumulation in the lungs over time. The fluid
accumulation may precede or coincide with worsening of HF such as
episodes of HF decompensation. The HF decompensation may be
characterized by pulmonary or peripheral edema, reduced cardiac
output, and symptoms such as fatigue, shortness of breath, and the
like.
SUMMARY
[0005] Ambulatory medical devices may be used for monitoring HF
patient and detecting worsening cardiac conditions such as a
worsening heart failure (WHF) event. Examples of such ambulatory
medical devices may include implantable medical devices (IMD),
subcutaneous medical devices, wearable medical devices or other
external medical devices. The ambulatory medical devices may
include physiological sensors which may be configured to sense
electrical activity and mechanical function of the heart. The
ambulatory medical devices may deliver therapy such as electrical
stimulations to target tissues or organs, such as to restore or
improve the cardiac function. Some of these devices may provide
diagnostic features, such as using transthoracic impedance or other
sensor signals to detect a disease or a disease condition. For
example, fluid accumulation in the lungs decreases the
transthoracic impedance due to the lower resistivity of the fluid
than air in the lungs.
[0006] Detection of worsening cardiac conditions may be based on a
detected change of a sensor signal (such as a thoracic impedance
signal) from a reference signal. An ideal detector of worsening
cardiac conditions, such as a WHF event, may have one or more of a
high sensitivity, a high specificity, a low false positive rate
(FPR), or a high positive predictive value (PPV). The sensitivity
may be represented as a percentage of actual WHF events that are
correctly recognized by a detection method. The specificity may be
represented as a percentage of actual non-WHF events that are
correctly recognized as non-WHF events by the detection method. The
FPR may be represented as a frequency of false positive detections
of WHF events per patient within a specified time period (e.g., a
year). The PPV may be represented as a percentage of the detected
WHF events, as declared by the detection method, which are actual
WHF events. A high sensitivity may help ensure timely intervention
to a patient with an impending WHF episode, whereas a high
specificity and a high PPV may avoid unnecessary intervention and
reduce false alarms.
[0007] Frequent monitoring of CHF patients and timely and accurate
detection of WHF events may reduce cost associated with HF
hospitalization. CHF patients, however, may be exposed to different
degrees of risks of developing a future WHF event. Therefore,
identification of patients at relatively higher risks may ensure
more effective and timely treatment, improve the prognosis and
patient outcome, and avoid unnecessary medical intervention and
reduce healthcare cost.
[0008] This document discusses, among other things, a patient
management system for detecting worsening cardiac events such as
WHF events that based at least on identified patient risks of
developing future WHF events. The system discussed herein may
include sensor circuits to sense physiological signals and
processors to generate from the physiological signals first and
second signal metrics. The system may include a risk stratifier
circuit to produce a cardiac risk indication. The system may use at
least the first signal metric to generate a primary detection
indication, and use at least the second signal metric and the risk
indication to generate a secondary detection indication. The risk
indication may be used to modulate the second signal metric. A
detector circuit may detect the worsening cardiac event using the
primary and secondary detection indications.
[0009] In Example 1, a system for detecting a worsening cardiac
event in a patient is disclosed. The system may comprise sensor
circuits including sense amplifier circuits to sense a first
physiological signal and a second physiological signal, a signal
processor circuit configured to generate a first signal metric from
the first physiological signal and a second signal metric from the
second physiological signal, a risk stratifier circuit configured
to produce a risk indication indicating a risk of the patient
developing a future worsening cardiac event, and a detector circuit
coupled to the signal processor circuit and the risk stratifier
circuit. The detector circuit may be configured to generate a
primary detection indication using at least the first signal metric
and a secondary detection indication using at least the second
signal metric and the risk indication, and to detect the worsening
cardiac event using the primary and secondary detection
indications.
[0010] Example 2 may include, or may optionally be combined with
the subject matter of Example 1 to optionally include, an output
circuit that may generate an alert in response to the detection of
the worsening cardiac event.
[0011] Example 3 may include, or may optionally be combined with
the subject matter of one or any combination of Examples 1 or 2 to
include, the first signal metric that may include a heart sound
signal metric and the second signal metric includes a respiratory
signal metric. The heart sound signal metric may include a third
heart sound (S3) intensity or a ratio of a third heart sound (S3)
intensity to a reference heart sound intensity, and the respiratory
signal metric may include a respiration rate measurement, a tidal
volume measurement, or a ratio of the respiration rate to the tidal
volume measurement.
[0012] Example 4 may include, or may optionally be combined with
the subject matter of one or any combination of Examples 1 through
3 to include, the detector circuit that may detect the worsening
cardiac event using a decision tree including the primary and
secondary detection indications. The secondary detection indication
may be generated based on a sub-decision tree included in the
decision tree. The sub-decision tree may include the risk
indication and a detection based on at least the second signal
metric.
[0013] Example 5 may include, or may optionally be combined with
the subject matter of Example 4 to optionally include, the sensor
circuits that may further include a third sense amplifier circuit
to sense a third physiological signal and the sub-decision tree
that may further include a detection based on the third
physiological signal. The detector circuit may be configured to
generate the secondary detection indication using the risk
indication if the decision based on the second physiological signal
indicates a detection of the worsening cardiac event, or generate
the secondary detection indication using the detection based on the
third physiological signal if the decision based on the second
physiological signal indicates no detection of the worsening
cardiac event.
[0014] Example 6 may include, or may optionally be combined with
the subject matter of one or any combination of Examples 1 through
5 to include, the primary or secondary detection indication that
may include a Boolean-logic or fuzzy-logic combination of two or
more signal metrics, or the risk indication that may include a
Boolean-logic or fuzzy-logic combination of two or more risk
indications.
[0015] Example 7 may include, or may optionally be combined with
the subject matter of one or any combination of Examples 1 through
6 to include, the detector circuit that may generate a composite
signal trend using a combination of the first signal metric and the
second signal metric modulated by the risk indication, and detect
the worsening cardiac event in response to the composite signal
trend satisfying a specified condition.
[0016] Example 8 may include, or may optionally be combined with
the subject matter of Example 7 to optionally include, the
modulation of the second signal metric that may include a temporal
change of the second signal metric weighted by the risk
indication.
[0017] Example 9 may include, or may optionally be combined with
the subject matter of Example 7 to optionally include, the
modulation of the second signal metric that may include a temporal
change of the second signal metric sampled when the risk indication
satisfies a specified condition.
[0018] Example 10 may include, or may optionally be combined with
the subject matter of one or any combination of Examples 1 through
9 to include, the second signal metric that is more sensitive and
less specific to the worsening cardiac event than the first signal
metric.
[0019] In Example 11, a system for identifying a patient's risk of
developing a future worsening cardiac disease is disclosed. The
system may comprise sensor circuits, a signal processor circuit, a
risk stratifier circuit coupled to the signal processor circuit,
and an output circuit. The sensor circuits may include sense
amplifier circuits to sense first, second, and third physiological
signals. The signal processor circuit may generate a first signal
metric from the first physiological signal, a second signal metric
from the second physiological signal, and a third signal metric
from the second physiological signal. The risk stratifier circuit
generate a primary cardiac risk indication using at least the first
signal metric, a secondary cardiac risk indication using at least
the second and third signal metrics, and a composite cardiac risk
indication using both the primary and secondary cardiac risk
indications. The output circuit may provide the composite cardiac
risk indication to a clinician or a process.
[0020] Example 12 may include, or may optionally be combined with
the subject matter of Example 11 to optionally include, the risk
stratifier circuit that may generate a secondary cardiac risk
indication using a plurality of measurements of the second signal
metric during a time period when the third signal metric satisfies
a specified condition.
[0021] Example 13 may include, or may optionally be combined with
the subject matter of one or any combination of Examples 11 or 12
to include, the signal processor circuit that may generate a first
plurality of measurements of the first signal metric and a second
plurality of measurements of the second signal metric. The risk
stratifier circuit may generate the primary cardiac risk indication
including a first statistic of the first plurality of measurements
of the first signal metric, and the secondary cardiac risk
indication including a second statistic of the second plurality of
measurements of the second signal metric. The risk stratifier
circuit may generate the composite cardiac risk indication using a
combination of the first statistic of the first signal metric and
the second statistic of the second signal metric.
[0022] Example 14 may include, or may optionally be combined with
the subject matter of one or any combination of Examples 11 through
13 to include, a fusion model selector circuit that may select a
fusion model from a plurality of candidate fusion models based on
signal quality of the first, second, and third physiological
signals. The risk stratifier circuit may generate the composite
cardiac risk indication using both the primary and secondary
cardiac risk indications according to the selected fusion
model.
[0023] Example 15 may include, or may optionally be combined with
the subject matter of one or any combination of Examples 11 through
14 to include, the risk stratifier circuit that may transform the
composite cardiac risk indication using a sigmoid function.
[0024] In Example 16, a method for detecting a worsening cardiac
event in a patient is disclosed. The method may include steps of
sensing, via sensor circuits, first and second physiological
signals; generating a first signal metric from the first
physiological signal and a second signal metric from the second
physiological signal; producing a risk indication indicating a risk
of the patient developing a future worsening cardiac event;
generating a primary detection indication using at least the first
signal metric, and a secondary detection indication using at least
the second signal metric and the risk indication; and detecting the
worsening cardiac event using the primary and secondary detection
indications.
[0025] Example 17 may include, or may optionally be combined with
the subject matter of Example 16 to optionally include, the method
of detecting the worsening cardiac event including using a decision
tree based on the primary and secondary detection indications. The
decision tree may include a sub-decision tree based on the risk
indication and a detection based on at least the second signal
metric.
[0026] Example 18 may include, or may optionally be combined with
the subject matter of Example 16 to optionally include, the primary
or secondary detection indication that may include a Boolean-logic
or fuzzy-logic combination of two or more signal metrics, or the
risk indication includes a Boolean-logic or fuzzy-logic combination
of two or more risk indications.
[0027] Example 19 may include, or may optionally be combined with
the subject matter of Example 16 to optionally include, steps of
generating a composite signal trend using a combination of the
first signal metric and the second signal metric modulated by the
risk indication, wherein the worsening cardiac event is detected in
response to the composite signal trend satisfying a specified
condition.
[0028] Example 20 may include, or may optionally be combined with
the subject matter of Example 19 to optionally include, the
modulation of the second signal metric that may include a scaled
temporal change of the second signal metric weighted by the risk
indication, or a sampled temporal change of the second signal
metric when the risk indication satisfies a specified
condition.
[0029] Example 21 may include, or may optionally be combined with
the subject matter of Example 16 to optionally include, the method
of producing the risk indication that may include generating a
primary cardiac risk indication using at least a first signal
metric for cardiac risk assessment and a secondary cardiac risk
indication using at least second and third signal metrics for
cardiac risk assessment, and generating a composite cardiac risk
indication using both the primary and secondary cardiac risk
indications.
[0030] Example 22 may include, or may optionally be combined with
the subject matter of Example 21 to optionally include, the method
of generating the secondary cardiac risk indication which may
include taking a plurality of measurements of the second signal
metric during a time period when the third signal metric satisfies
a specified condition.
[0031] Example 23 may include, or may optionally be combined with
the subject matter of Example 21 to optionally include, the method
of producing the risk indication that may include transforming the
composite cardiac risk indication using a sigmoid function.
[0032] The systems, devices, and methods discussed in this document
may improve the medical technology of automated monitoring of
patients with worsening heart failure (WHF). The detection of WHF
based on primary and secondary detections and a cardiac risk
indication may enhance the performance and functionality of a
medical system or an ambulatory medical device for detecting WHF.
In certain examples, the enhanced device functionality may include
more timely detection of WHF with increased accuracy (e.g., lower
false positive rate and higher positive predictive value) at little
to no additional cost. The improvement in system performance and
functionality, provided by the present systems and methods, can
reduce healthcare costs associated with management and
hospitalization of heart failure patients. The systems, devices,
and methods discussed in this document also allow for more
efficient device memory usage, such as by storing cardiac risk
indications and signal metrics that are clinically more relevant to
WHF. As fewer false positive detections are provided, device
battery life can be extended, fewer unnecessary drugs and
procedures may be scheduled, prescribed, or provided, and an
overall system cost savings may be realized.
[0033] This Summary is an overview of some of the teachings of the
present application and not intended to be an exclusive or
exhaustive treatment of the present subject matter. Further details
about the present subject matter are found in the detailed
description and appended claims. Other aspects of the invention
will be apparent to persons skilled in the art upon reading and
understanding the following detailed description and viewing the
drawings that form a part thereof, each of which are not to be
taken in a limiting sense. The scope of the present invention is
defined by the appended claims and their legal equivalents.
BRIEF DESCRIPTION OF THE DRAWINGS
[0034] Various embodiments are illustrated by way of example in the
figures of the accompanying drawings. Such embodiments are
demonstrative and not intended to be exhaustive or exclusive
embodiments of the present subject matter.
[0035] FIG. 1 illustrates generally an example of a patient
management system and portions of an environment in which the
patient management system may operate.
[0036] FIG. 2 illustrates generally an example of a cardiac event
detection system for detecting a worsening cardiac event.
[0037] FIGS. 3A-D illustrate generally examples of secondary
detectors for generating a secondary detection indication based at
least on a second signal metric and the risk indication.
[0038] FIG. 4 illustrates generally an example of a risk stratifier
circuit for assessing a patient risk of developing a future
worsening cardiac event.
[0039] FIG. 5 illustrates generally an example of a secondary risk
generator for generating a cardiac risk indication based on
conditional sampling of a signal metric.
[0040] FIG. 6 illustrates generally an example of a method for
detecting a worsening cardiac event.
[0041] FIGS. 7A-B illustrate generally examples of decision trees
for detecting the worsening cardiac event.
[0042] FIG. 8 illustrates generally an example of a portion of a
method for detecting worsening cardiac event based at least the
first and second signal metrics.
[0043] FIG. 9 illustrates generally an example of a method for
cardiac risk assessment.
DETAILED DESCRIPTION
[0044] Disclosed herein are systems, devices, and methods for
detecting worsening cardiac conditions, including events indicative
of worsening heart failure. The WHF event may occur before
systematic manifestation of worsening of HF. The systems, devices,
and methods described herein may be used to determine a patient's
cardiac status as well as to track progression of the cardiac
condition such as worsening of a HF event. This system may also be
used in the context of HF comorbidities and worsening chronic
diseases such as pulmonary congestion, pneumonia, or renal
diseases, among others.
[0045] FIG. 1 illustrates generally an example of a patient
management system 100 and portions of an environment in which the
patient management system 100 may operate. The patient management
system 100 may include an ambulatory system 105 associated with a
patient body 102, an external system 125, and a telemetry link 115
providing for communication between the ambulatory system 105 and
the external system 125.
[0046] The ambulatory system 105 may include an ambulatory medical
device (AMD) 110 and a therapy delivery system such as a lead
system 108. The AMD 110 may include an implantable device that may
be implanted within the body 102 and coupled to a heart 101 via the
lead system 108. Examples of the implantable device may include,
but are not limited to, pacemakers, pacemaker/defibrillators,
cardiac resynchronization therapy (CRT) devices, cardiac remodeling
control therapy (RCT) devices, neuromodulators, drug delivery
devices, biological therapy devices, diagnostic devices, or patient
monitors, among others. The AMD 110 may alternatively or
additionally include subcutaneously implanted devices such as a
subcutaneous ICD or a subcutaneous diagnostic device, wearable
medical devices such as patch based sensing device, or other
external monitoring or therapeutic medical devices such as a
bedside monitor.
[0047] The lead system 108 may include one or more transvenously,
subcutaneously, or non-invasively placed leads or catheters. Each
lead or catheter may include one or more electrodes for delivering
pacing, cardioversion, defibrillation, neuromodulation, drug
therapies, or biological therapies, among other types of therapies.
In an example, the electrodes on the lead system 108 may be
positioned inside or on a surface of at least a portion of the
heart, such as a right atrium (RA), a right ventricle (RV), a left
atrium (LA), a left ventricle (LV), or any tissue between or near
the heart portions. The arrangements and uses of the lead system
108 and the associated electrodes may be determined based on the
patient need and the capability of the AMD 110. In some examples,
the AMD 110 may include one or more un-tethered electrodes
associated with an outer surface of the AMD 110, and the AMD 110
and the associated un-tethered electrodes may be configured to be
deployed to a target cardiac site or other tissue site.
[0048] The AMD 110 may house an electronic circuit for sensing a
physiological signal, such as by using a physiological sensor or
the electrodes associated with the lead system 108. Examples of the
physiological signal may include one or more of electrocardiogram,
intracardiac electrogram, arrhythmia, heart rate, heart rate
variability, intrathoracic impedance, intracardiac impedance,
arterial pressure, pulmonary artery pressure, left atrial pressure,
RV pressure, LV coronary pressure, coronary blood temperature,
blood oxygen saturation, one or more heart sounds, intracardiac or
endocardial acceleration, physical activity or exertion level,
physiological response to activity, posture, respiration, body
weight, or body temperature. The AMD 110 may initiate or adjust
therapies based on the sensed physiological signals.
[0049] The patient management system 100 may include a worsening
cardiac event detector circuit 160 provided for patient management
using at least diagnostic data acquired by the ambulatory system
105. The worsening cardiac event detector circuit 160 may analyze
the diagnostic data for patient monitoring, risk stratification,
and detection of events such as WHF or one or more HF
comorbidities. In an example as illustrated in FIG.1, the worsening
cardiac event detector circuit 160 may be substantially included in
the AMD 110. Alternatively, the worsening cardiac event detector
circuit 160 may be substantially included in the external system
125, or be distributed between the ambulatory system 105 and the
external system 125.
[0050] The external system 125 may be used to program the AMD 110.
The external system 125 may include a programmer, a communicator,
or a patient management system that may access the ambulatory
system 105 from a remote location and monitor patient status and/or
adjust therapies. By way of example and not limitation, and as
illustrated in FIG.1, the external system 125 may include an
external device 120 in proximity of the AMD 110, a remote device
124 in a location relatively distant from the AMD 110, and a
telecommunication network 122 linking the external device 120 and
the remote device 124. The telemetry link 115 may be an inductive
telemetry link, or a radio-frequency (RF) telemetry link. The
telemetry link 115 may provide for data transmission from the AMD
110 to the external system 125. This may include, for example,
transmitting real-time physiological data acquired by the AMD 110,
extracting physiological data acquired by and stored in the AMD
110, extracting patient history data such as data indicative of
occurrences of arrhythmias, occurrences of decompensation, and
therapy deliveries recorded in the AMD 110, and extracting data
indicating an operational status of the AMD 110 (e.g., battery
status and lead impedance). The telemetry link 115 may also provide
for data transmission from the external system 125 to the AMD 110.
This may include, for example, programming the AMD 110 to perform
one or more of acquiring physiological data, performing at least
one self-diagnostic test (such as for a device operational status),
delivering at least one therapy, or analyzing data associated with
patient health conditions such as progression of heart failure.
[0051] Portions of the AMD 110 or the external system 125 may be
implemented using hardware, software, or any combination of
hardware and software. Portions of the AMD 110 or the external
system 125 may be implemented using an application-specific circuit
that may be constructed or configured to perform one or more
particular functions, or may be implemented using a general-purpose
circuit that may be programmed or otherwise configured to perform
one or more particular functions. Such a general-purpose circuit
may include a microprocessor or a portion thereof, a
microcontroller or a portion thereof, or a programmable logic
circuit, or a portion thereof. For example, a "comparator" may
include, among other things, an electronic circuit comparator that
may be constructed to perform the specific function of a comparison
between two signals or the comparator may be implemented as a
portion of a general-purpose circuit that may be driven by a code
instructing a portion of the general-purpose circuit to perform a
comparison between the two signals.
[0052] FIG. 2 illustrates generally an example of a cardiac event
detection system 200 for detecting worsening cardiac conditions,
such as a WHF event. The cardiac event detection system 200 may
include one or more of sensor circuits 210, a signal processor
circuit 220, a risk stratifier circuit 230, a detector circuit 240,
a controller circuit 250, and a user interface 260. In an example,
a portion of the cardiac event detection system 200 may be
implemented within the AMD 110, distributed between two or more
implantable or wearable medical devices (such as an implantable
medical device and a subcutaneous medical device), or distributed
between the AMD 110 and the external system 125.
[0053] The sensor circuits 210 may include at least a first sense
amplifier circuit 212 to sense a first physiological signal and a
second sense amplifier circuit 214 to sense a different second
physiological signal. The first and second physiological signals
may each be indicative of intrinsic physiological activities,
evoked physiological activities when the heart or other tissues are
stimulated in accordance with a specified stimulation
configuration, or physiological activities under other specified
conditions. The first or second sense amplifier circuit may be
coupled to one or more electrodes such as on the lead system 108,
or one or more implantable, wearable, or other ambulatory
physiological sensors, to sense the physiological signal(s).
Examples of physiological sensors may include pressure sensors,
flow sensors, impedance sensors, accelerometers, microphone
sensors, respiration sensors, temperature sensors, or blood
chemical sensors, among others. Examples of the physiological
signals sensed by the sensor circuits 210 may include
electrocardiograph (ECG), an electrogram (EGM), an intrathoracic
impedance signal, an intracardiac impedance signal, an arterial
pressure signal, a pulmonary artery pressure signal, a RV pressure
signal, a LV coronary pressure signal, a coronary blood temperature
signal, a blood oxygen saturation signal, central venous pH value,
a heart sound (HS) signal, a posture signal, a physical activity
signal, or a respiration signal, among others. In some examples,
the first or second sense amplifier may retrieve a respective
physiological signal stored in a storage device such as an external
programmer, an electronic medical record (EMR) system, or a memory
unit, among other storage devices.
[0054] The signal processor circuit 220, coupled to the
physiological sensor circuit 210, may include a first filter
circuit 222 to filter the first sensed physiological signal to
produce a trend of a first signal metric X1 D for detection, and a
second filter circuit 224 to filter the second sensed physiological
signal to produce a trend of a second signal metric X2.sub.D for
detection. The first and second signal metrics X1.sub.D and
X2.sub.D may each include statistical parameters extracted from the
sensed physiological signal, such as signal mean, median, or other
central tendency measures or a histogram of the signal intensity,
among others. The first and second signal metrics may additionally
or alternatively include morphological parameters such as maximum
or minimum within a specified time period such as a cardiac cycle,
a specific posture or an activity intensity, positive or negative
slope or higher order statistics, or signal power spectral density
at a specified frequency range, among other morphological
parameters.
[0055] Depending on the respective sensed physiological signal,
various first and second signal metrics may be generated. In an
example, a thoracic or cardiac impedance signal may be sensed using
the electrodes on the lead system 108, and impedance metrics may
include thoracic impedance magnitude within a specified frequency
range obtained from. In an example, a heart sound (HS) signal may
be sensed from an accelerometer, a microphone, or an acoustic
sensor coupled to the AMD 110, and HS metrics may include
intensities of first (S1), second (S2), third (S3), or fourth (S4)
heart sound components or a relative intensity such as a ratio
between two heart sound components, timing of one of the S1, S2,
S3, or S4 heart sound components relative to a fiducial point such
as a P wave, Q wave, or R wave in an ECG. In an example, the
accelerometer may be associated with a lead such as of the lead
system 108 or on a surface of an intracardiac pacing device located
inside the heart. The accelerometer may be configured to sense
intracardiac or endocardial accelerations indicative of heart
sounds. In an example, a respiration signal may be sensed using an
impedance sensor or an accelerometer, and the respiratory metric
may include a respiratory rate, a tidal volume, a minute
ventilation, a posture, or a rapid-shallow breathing index (RSBI)
computed as a ratio of a respiratory rate measurement to a tidal
volume measurement. In another example, a physical activity signal
may be sensed using an accelerometer, and the activity metrics may
include physical activity intensity, or a time duration when the
activity intensity is within a specified range or above a specified
threshold. In yet another example, a blood pressure signal may be
sensed using a pressure sensor, and the pressure metrics may
include systolic blood pressure, diastolic blood pressure, mean
arterial pressure, and the timing metrics of these pressure
measurements with respect to a fiducial point.
[0056] In an example, the second signal metric X2.sub.D may differ
from the first signal metric X1.sub.D such that X2.sub.D may be
more sensitive and less specific to a worsening cardiac event (such
as a WHF event) than X1.sub.D. Relative sensitivity or specificity
may be based on detection performance of the signal metrics across
a cohort of patients. In an example, the second signal metric
X2.sub.D may be evaluated when the first signal metric X1.sub.D
does not indicate a detection of worsening cardiac event. A more
sensitive X2.sub.D may be used to reduce the false negative
detections of the worsening cardiac event based solely on X1.sub.D.
In an example, the first signal metric X1.sub.D may include a HS
metric such as a S3 heart sound intensity or a ratio of S3
intensity to a HS reference intensity. Examples of the reference
intensity may include a first heart sound (S1) intensity, a second
heart sound (S2) intensity, or heart sound energy during a
specified time period within a cardiac cycle. Other examples of the
second signal metric X2.sub.D may include thoracic impedance
magnitude, or respiratory metric such as respiratory rate
measurement, a minute ventilation measurement, a tidal volume
measurement, or an RSBI.
[0057] A signal metric trend may be formed using multiple
measurements of the signal metric during a specified time period.
In an example, the signal metric trend may include a daily trend
including daily measurement of a signal metric over a specified
number of days. Each daily measurement may be determined as a
central tendency of a plurality of measurements obtained within a
day. In an example, a thoracic impedance trend may be generated
using portions of the received impedance signal during identical
phases of a cardiac cycle such as within a certain time window
relative to R-wave in a ECG signal), or at identical phases of a
respiratory cycle such as within an inspiration phase or an
expiration phase of a respiration signal. This may minimize or
attenuate the interferences such as due to cardiac or respiratory
activities, in the impedance measurements. The thoracic impedance
trend may be generated using impedance measurements collected
during one or more impedance acquisition and analysis sessions. In
an example, an impedance acquisition and analysis session may start
between approximately 5 a.m. and 9 a.m. in the morning, and lasts
for approximately 2-8 hours. In another example, the impedance
acquisition and analysis session may be programmed to exclude
certain time periods, such as night time, or when the patient is
asleep. The impedance parameter may be determined as a median of
multiple impedance measurements acquired during the impedance
acquisition and analysis session.
[0058] The risk stratifier circuit 230 may produce a risk
indication (R) indicating a risk of the patient developing a future
worsening cardiac event. The risk indication may have categorical
values indicating risk degrees such as "high", "medium", or "low"
risks, or alternatively numerical risk scores within a specified
range. The risk scores may have discrete values (e.g., integers
from 0 through 5) or continuous values (e.g., real numbers between
0 and 1), where a larger risk score indicates a higher risk.
[0059] In an example, the risk indication may be at least partially
automatically retrieved from a memory that stores the patient's
up-to-date risk information. In an example, the risk stratifier
circuit 230 may determine the risk indication by analyzing a
physiological signal, such as by using one or more signal metrics
generated by the signal processor circuit 220 from the
physiological signal. The physiological signal or the signal
metrics (denoted by X1.sub.R, X2.sub.R, etc.) for assessing cardiac
risk may be different from the physiological signals or the signal
metrics used for detecting the cardiac event (such as the first and
second signal metrics X1.sub.D and X2.sub.D generated at the first
and second filters 222 and 224). In another example, at least one
signal metric may be used for both cardiac risk assessment and for
cardiac event detection. By way of non-limiting examples, the
signal metrics for cardiac risk assessment may include intensity of
a heart sound component such as S3 heart sound, a respiratory rate,
a tidal volume measurement, a thoracic impedance magnitude, or
physical activity intensity, among others. The risk indication
generated by the risk stratifier circuit 230 may be confirmed or
edited by a system user such as via the user interface 260.
Examples of the risk stratifier circuit for assessing a cardiac
risk are discussed below, such as with reference to FIGS. 4-5.
[0060] In some examples, the risk stratifier circuit 230 may
determine the risk indication using at least information about
patient's overall health conditions, clinical assessments, or other
current and historic diseases states that may increase or decrease
the patient's susceptibility to future WHF. For example, following
a WHF event, a patient may have an elevated risk of developing
another WHF event or being re-hospitalized. The risk stratifier
circuit 230 may determine the risk indication using time elapsed
since the last WHF event. In another example, a patient having a
medical history of atrial fibrillation may be more susceptible to a
future WHF event. The risk stratifier circuit 230 may determine the
risk indication using a trend consisting of the time spent in AF
each day. In another example, the risk indication may be determined
based on the number or severity of one or more comorbid conditions,
such as HF comorbidities.
[0061] The detector circuit 240 may be coupled to the signal
processor circuit 220 and the risk stratifier circuit 230 to detect
a worsening cardiac event, such as a WHF event. The detector
circuit 240 may be implemented as a part of a microprocessor
circuit. The microprocessor circuit may be a dedicated processor
such as a digital signal processor, application specific integrated
circuit (ASIC), microprocessor, or other type of processor for
processing information including the physiological signals received
from the sensor circuits 210. Alternatively, the microprocessor
circuit may be a general purpose processor that may receive and
execute a set of instructions of performing the functions, methods,
or techniques described herein.
[0062] The detector circuit 240 may include circuit sets comprising
one or more other circuits or sub-circuits, such as a primary
detector circuit 242, a secondary detector circuit 244, and a
detection fusion circuit 246, as illustrated in FIG. 2. These
circuits may, alone or in combination, perform the functions,
methods, or techniques described herein. In an example, hardware of
the circuit set may be immutably designed to carry out a specific
operation (e.g., hardwired). In an example, the hardware of the
circuit set may include variably connected physical components
(e.g., execution units, transistors, simple circuits, etc.)
including a computer readable medium physically modified (e.g.,
magnetically, electrically, moveable placement of invariant massed
particles, etc.) to encode instructions of the specific operation.
In connecting the physical components, the underlying electrical
properties of a hardware constituent are changed, for example, from
an insulator to a conductor or vice versa. The instructions enable
embedded hardware (e.g., the execution units or a loading
mechanism) to create members of the circuit set in hardware via the
variable connections to carry out portions of the specific
operation when in operation. Accordingly, the computer readable
medium is communicatively coupled to the other components of the
circuit set member when the device is operating. In an example, any
of the physical components may be used in more than one member of
more than one circuit set. For example, under operation, execution
units may be used in a first circuit of a first circuit set at one
point in time and reused by a second circuit in the first circuit
set, or by a third circuit in a second circuit set at a different
time.
[0063] The primary detector 242 may generate a primary detection
indication D1 using at least the first signal metric X1.sub.D. The
detection may be based on temporal change of the first signal
metric X1.sub.D, such as a relative difference of the signal metric
from a reference level representing a signal metric baseline. In an
example, the relative difference may be calculated as a difference
between a central tendency of multiple measurements of X1.sub.D
within a short-term window and a central tendency of multiple
measurements of X1.sub.D within a long-term window preceding the
short-term time window in time. The relative difference may be
compared to a specified condition (e.g., a threshold or a specified
range), and generate a binary primary detection indication D1 of
"1" if the relative difference satisfies the specified condition,
or "0" if the relative fails to satisfy the specified condition. In
lieu of binary detection indications, the primary detector 242 may
alternatively produce the detection indication D1 having real
numbers (such as between 0 and 1) indicative of confidence of
detection. The confidence may be proportional to the deviation of
the signal metric difference (e.g., .DELTA.X1.sub.C) from a
detection threshold.
[0064] The secondary detector 244 may generate a secondary
detection indication D2 using at least the second signal metric
X2.sub.D and the risk indication R. In an example, the secondary
detector 244 may calculate a relative difference (.DELTA.X2)
between a representative value of the second signal metric X2.sub.D
such as a central tendency of multiple measurements of X2.sub.D
within a short-term window and a baseline value such as a central
tendency of multiple measurements of X2.sub.D within a long-term
window preceding the short-term window in time. The secondary
detector 244 may compute the secondary detection indication D2
using a linear, nonlinear, or logical combination of the relative
difference (.DELTA.X2) and the risk indication R. The relative
difference (.DELTA.X2) may be modulated by the risk indications R.
Similar to the primary detection indication D1, the secondary
detection indication D2 may have a discrete value such as "0"
indicating no detection and a "1" indicating detection of the
worsening cardiac event based on .DELTA.X2, or continuous values
within a specified range such as indicating the confidence of the
detection. Examples of the secondary detector using the second
signal metric X2.sub.D and the risk indication R are discussed
below, such as with reference to FIGS. 3A-D.
[0065] The detection fusion circuit 246 may generate a composite
detection indication (CDI) using the primary detection indication
D1 and the secondary detection indication D2. In an example, the
detection fusion circuit 246 may generate the CDI using a decision
tree. The decision tree may be implemented as a set of circuits,
such as logic circuit, that perform logical combinations of at
least the primary and secondary detection indications D1 and D2.
Alternatively, at least a portion of the decision tree may be
implemented in a microprocessor circuit, such as a digital signal
processor or a general purpose processor, which may receive and
execute a set of instructions including logical combinations of at
least the primary and secondary detection indications D1 and
D2.
[0066] The decision tree for detecting the worsening cardiac event
may include a tiered detection process comprising the primary
detection indication D1, and subsequent detection indication D2 if
the primary detection indication D1 indicates no detection of the
worsening cardiac event. In an example, according to the decision
tree, the CDI may be expressed as Boolean logic "OR" between D1 and
D2 each satisfying respective conditions, as shown in Equation
(1):
CDI=(D1) OR (D2) (1)
In an example, D1 is based on a heart sound metric of a ratio of S3
to S1 heart sound intensity (S3/S1), and D2 is based on a metric of
thoracic impedance magnitude (Z) or a rapid-shallow breathing index
(RSBI).
[0067] As to be discussed below with reference to FIGS. 3A-D, the
secondary detection indication D2 may be generated as a logical or
linear combinations of the second signal metric X2.sub.D and the
risk indication R. In an example, the logical combination of the
risk indication (R) and the second signal metric X2.sub.D may be
represented by a sub-decision tree included in the decision tree
for detecting the worsening cardiac event. In an example, the risk
indication is evaluated only when the second signal metric X2.sub.D
indicates a detection of the worsening cardiac event (such as when
S2.sub.D satisfies a detection condition). Accordingly, the
secondary detection indication D2 may be represented as Boolean
logic "AND" between X2.sub.D and R, that is, D2=X2.sub.D AND R.
Substituting the logical formula of D2 into Equation (1) yields
Equation (2) corresponding to the decision tree that includes the
sub-decision tree for determining D2:
CDI=(X1.sub.D) OR ((X2.sub.D) AND (R)) (2)
In an example, the second signal metric X2.sub.D includes the
thoracic impedance (Z) and the risk indication (R) is assessed
using S3 heart sound, such as a central tendency or variability of
S3 intensity measurements. The CDI for detecting the worsening
cardiac event may be expressed as in Equation (3) below, where T1,
T2 and T3 denote thresholds for the respective signal metrics:
CDI = { 1 , if ( S 3 S 1 > T 1 ) OR ( ( Z > T 2 ) AND ( S 3
> T 3 ) ) 0 , else ( 3 ) ##EQU00001##
[0068] In addition to or in lieu of the decision tree, the
detection circuit 240 may generate the CDI from a composite signal
trend (cY) such as a linear or a nonlinear combination of the
relative difference of the first signal metric X1.sub.D, and the
relative difference of the second signal metric X2.sub.D modulated
by the risk indications R. Examples of modulation of second signal
metric may include scaling the second signal metric X2.sub.D by the
risk indication R, or sampling X2.sub.D conditionally upon the risk
indication R satisfying a specified condition. Modulations such as
scaling and conditional sampling of X2.sub.D are discussed below
with reference to FIGS. 3A-B.
[0069] To account for differences in signal properties (such as
signal range or signal change or rate of change) of various signal
metrics, the signal metrics may be transformed into a unified scale
such that they may be easily comparable or combined. In an example,
the primary detector 242 may transform the relative difference of
X1.sub.D into a first sequence of transformed indices
Y1=f.sub.1(X1.sub.D). The secondary detector 244 may similarly
transform the relative difference of X2.sub.D into a second
sequence of transformed indices Y2=f.sub.2(X2.sub.D) within the
same specified range. In an example, the transformations f.sub.1
and f.sub.2 may each include a use of respective codebook that maps
quantized magnitude of respective signal metric into numerical
indices within a specified range, where a larger code indicates a
higher signal magnitude. In an example, the transformed indices Y1
or Y2 may be obtained from a transformation of linear or nonlinear
combination of more than one signal metrics.
[0070] The secondary detector 244 may modulate the transformed
indices Y2 by the risk indication R, denoted by Y2|.sub.R, and the
detection fusion circuit 246 may generate the composite signal
trend cY by combining Y1 and Y2|.sub.R, such as a linear
combination as shown in Equation (4) below:
cY=Y1+Y2|R (4)
In an example, the modulation includes a multiplication operation
between Y2 and R. In another example, the modulation includes
conditionally-sampling of Y2 upon R satisfying a specified
condition. Examples of the secondary detector using the second
signal metric X2.sub.D and the risk indication R are discussed
below, such as with reference to FIGS. 3A-D. The detection fusion
circuit 246 may determine the CDI by comparing the composite signal
trend cY to a threshold, as shown in Equation (5) below, where T1
denotes the threshold for cY:
CDI = { 1 , if ( Y 1 + Y 2 R ) > T 1 0 , else ( 5 )
##EQU00002##
[0071] The controller circuit 250 may control the operations of the
sensor circuits 210, the signal processor circuit 220, the risk
stratifier circuit 230, the detector circuit 240, the user
interface unit 260, and the data and instruction flow between these
components. In an example as previously discussed, the controller
circuit 250 may configure the operations of the secondary detector
243, such as a combination of the second signal metric and the risk
indication for generating the secondary detection indication
D2.
[0072] The user interface 260 may include a user input module 261
and an output module 261. In an example, at least a portion of the
user interface unit 260 may be implemented in the external system
120. The user input module 261 may be coupled to one or more user
input device such as a keyboard, on-screen keyboard, mouse,
trackball, touchpad, touch-screen, or other pointing or navigating
devices. The input device may enables a system user (such as a
clinician) to program the parameters used for sensing the
physiological signals, assessing risk indications, and detecting
worsening cardiac event. The output module 262 may generate a
human-perceptible presentation of the composite detection
indication (CDI), such as displayed on the display. The
presentation may include other diagnostic information including the
physiological signals and the signals metrics, the primary and
secondary detection indications, the risk indications, as well as
device status such as lead impedance and integrity, battery status
such as remaining lifetime of the battery, or cardiac capture
threshold, among others. The information may be presented in a
table, a chart, a diagram, or any other types of textual, tabular,
or graphical presentation formats, for displaying to a system user.
Additionally or alternatively, the CDI may be presented to the
process such as an alert circuit for producing an alert in response
to the CDI satisfies a specified condition. The alert may include
audio or other human-perceptible media format.
[0073] In some examples, the cardiac event detection system 200 may
additionally include a therapy circuit 270 configured to deliver a
therapy to the patient in response to one or more of the primary or
secondary detection indications or the composite detection
indication. Examples of the therapy may include electrostimulation
therapy delivered to the heart, a nerve tissue, other target
tissues in response to the detection of the target physiological
event, or drug therapy including delivering drug to a tissue or
organ. In some examples, the primary or secondary detection
indications or the composite detection indication may be used to
modify an existing therapy, such as adjusting a stimulation
parameter or drug dosage.
[0074] FIGS. 3A-D illustrate generally examples of secondary
detectors 310, 320, 330 and 340 for generating a secondary
detection indication (D2) based at least on a second signal metric
X2.sub.D such as produced at the second filter 224 and the risk
indication (R) such as produced at the risk stratifier circuit 230.
The secondary detectors 310, 320, 330 and 340 may be embodiments of
the secondary detector 244 in FIG. 2. The secondary detection
indication D2 may be a linear or a nonlinear combination of the
temporal change of a second signal metric X2.sub.D and the risk
indication R. In an example as illustrated in FIG. 3A, the
secondary detector 310 may include a multiplier circuit 312 that
multiplies the temporal change by the risk indication R to produce
the secondary detection indication D2. In an example, the risk
indication R may take binary values "0" or "1", such as to gate the
contribution of the second signal metric X2.sub.D to the secondary
detection indication D2 (e.g., using R to turn on the D2 if R=1, or
to turn off D2 if R=0). In another example, the risk indication R
may take real numbers such as between 0 and 1, such as to weight
the contribution of the second signal metric X2.sub.D to the
secondary detection indication D2. In an example, the risk
indication from the risk stratifier circuit 230 includes a signal
metric trend to modulate the second signal metric X2.sub.D or a
transformation of a temporal change of X2.sub.D, such as Y2|.sub.R
as shown in Equation (4). The multiplier circuit 312 may produce a
modulated second signal metric (such as Y2*R), which would be used
by the detection fusion circuit 246 for generating the composite
signal trend for detecting worsening cardiac event.
[0075] FIG. 3B illustrates the secondary detector 320 that may
generate the secondary detection indication D2 using the second
signal metric X2.sub.D when the risk indication satisfies a
specified condition. The secondary detector 320 may include a
sampling circuit 322, a comparator 324, and a conditional detector
326. The comparator 324 may compare the risk indication R to
specified condition such as a specified range. The sampling circuit
322 may sample the second signal metric X2.sub.D only when the risk
indication R satisfies the specified condition, such as when the
signal metric used for risk assessment falls within a specified
range. In an example, the second signal metric X2.sub.D may include
a respiratory rate trend, and the risk indication may include
physical activity intensity. The sampling circuit 322 may sample
the respiratory rate trend during a time period when the physical
activity intensity exceeds a specified threshold. The conditional
detector 326 may generate the secondary detection indication D2
using a statistical measure, such as a central tendency or a
variability, of the sampled RR measurements. In an example, the
risk indication from the risk stratifier circuit 230 includes a
signal metric trend to modulate the second signal metric X2.sub.D
or a transformation of a temporal change of X2.sub.D, such as
Y2|.sub.R as shown in Equation (4). The sampling circuit 322 may
produce a modulated second signal metric, including the
conditionally sampled X2.sub.D or conditionally sampled transformed
signal metric Y2 upon R satisfying a specified condition. The
conditionally sampled X2.sub.D or Y2 would be used by the detection
fusion circuit 246 for generating the composite signal trend for
detecting worsening cardiac event.
[0076] FIG. 3C illustrates the secondary detector 330 that may
generate the secondary detection indication D2 using a logical
combination of the second signal metric X2.sub.D and the risk
indication R, such as the sub-decision tree included in the
decision tree for detecting the worsening cardiac event, as
previously discussed with reference to FIG. 2. The secondary
detector 330 may include a comparator 332 to compare the temporal
change of the second signal metric X2.sub.D to a threshold, a
comparator 334 to compare the risk indication R to a threshold, and
a logical combination circuit 336 to generate a detection decision
based on the sub-decision tree. In an example, if the second signal
metric X2.sub.D indicates a detection of the worsening cardiac
event (such as falling with a range), the logical combination
circuit 336 may use the risk indication R to confirm the detection
based on X2.sub.D. In some examples, the secondary detector 330 may
additionally receive a third signal metric X3.sub.D generated from
the same or a different physiological signal from which X2.sub.D is
generated. The sub-decision tree may additionally include the
detection according to the third signal metric X3.sub.D. If the
second signal metric X2.sub.D indicates no detection of the
worsening cardiac event, the logical combination circuit 336 may
use X3.sub.D to generate the secondary detection indication D2.
[0077] FIG. 3D illustrates the secondary detector 340 that may
generate the secondary detection indication D2 using a fuzzy-logic
combination of the second signal metric X2.sub.D and the risk
indication R. Compared to the Boolean logic which takes crisp
decisions of "1" or "0" (such as based on threshold crossing), the
fuzzy-logic may take real numbers such as between 0 and 1. The
fuzzifier circuit 342 may partition the range of the signal metric
X2.sub.D and the range of risk indication R each into respective
fuzzy sets, and to transform the second signal metric X2.sub.D and
the risk indication R each into respective fuzzified
representations X2.sub.D' and R'. The fuzzified presentations
X2.sub.D' and R' may then be combined using fuzzy-logic operators,
including "minimum" or multiplication operator corresponding to the
Boolean operator "AND", "maximum" or addition operator
corresponding to the Boolean operator "OR", and "1-x" (where x
represents a fuzzified representation) corresponding to the Boolean
operator "NOT". In an example, the fuzzy-logic combination circuit
342 may compute a numerical D2 as "minimum" between the risk
indication (R) and the second signal metric X2.sub.D, that is,
D2=min(X2.sub.D, R), which corresponds to D2=X2.sub.D AND R in
Boolean-logic combination as in the secondary detector 330.
[0078] In an example, the fuzzy-logic combination circuit 342 may
combine the fuzzified presentations X2.sub.D'and R' using a hybrid
of the Boolean logic and fuzzy-logic combinations. For example, the
sub-decision tree as discussed in secondary detector 330 may
include a Boolean-logic combination, such that D2=(X2.sub.D') AND
(R'), while the X2.sub.D'or R' may each be determined as
fuzzy-logic combinations of two or more signal metrics. For
example, X2.sub.D'may be determined as a maximum between a temporal
change of thoracic impedance (.DELTA.Z) and a temporal change of
RSBI (ARSBI), that is, X2.sub.D'=max(Z, RSBI). In an example, R'
may be determined as a minimum of a central tendency or variability
of S3 intensity measurements S3, and the respiratory rate (RR)
variability, that is, R'=min (S3, RR). By substituting the
fuzzy-logic representations of X2.sub.D' and R' into the
Boolean-logic representation of D2, the secondary detection
indication D2 may be determined according to Equation (6)
below:
D2=(max (Z, RSBI)>T1) AND (min (S3, RR)>T2) (6)
[0079] FIG. 4 illustrates generally an example of a risk stratifier
circuit 400 for assessing a patient risk of developing a future
worsening cardiac event, such as a WHF event. The risk stratifier
circuit 400 may be an embodiment of the risk stratifier circuit
230. The risk stratifier circuit 400 may include one or more of a
primary risk generator 410, a secondary risk generator circuit 420,
an optional indication-based risk adjuster 440, and a blending
circuit 430. The primary risk generator 410 may be coupled to the
signal processor circuit 220 to receive a plurality of measurements
of a first signal metric 221 (X1.sub.R) for cardiac risk
assessment, and generate a primary cardiac risk indication (R1)
using at least X1.sub.R. The signal metric X1.sub.R may be
different from the first and second signal metrics X1.sub.D and
X2.sub.D used by the primary and secondary detectors 242 and 244
for detecting worsening cardiac event. In an example, the first
signal metric X1.sub.R may be extracted from a heart sound signal,
and include one of a S3 intensity, or a ratio of a S3 intensity to
a reference heart sound intensity such as S1 intensity, S2
intensity, or heart sound energy during a specified portion of the
cardiac cycle. The primary risk generator 410 may generate the
primary cardiac risk indication (R1) using a statistical measure,
such as a central tendency or a variability, of the plurality of
the measurements of the signal metric X1.sub.R.
[0080] The secondary risk generator 420 may generate a secondary
cardiac risk indication (R2) using a plurality of measurements of a
second signal metric 222 (X2.sub.R) and a plurality of measurements
of a third signal metric 223 (X3.sub.R) for cardiac risk
assessment, such as generated by the signal processor circuit 220.
The signal metrics X2.sub.R and X3.sub.R may be different from the
signal metric X1.sub.R for cardiac risk assessment, and may be
different from the signal metrics X1.sub.D and X2.sub.D for
detecting worsening cardiac event. In an example, the second signal
metric X2.sub.R for cardiac risk assessment may include a
respiration signal metric, such as a respiratory rate, a tidal
volume, or a rapid-shallow breathing index (RSBI) computed as a
ratio of the respiratory rate to the tidal volume. A patient who
breathes rapidly (high respiratory rate) and shallowly (low tidal
volume) tends to have a high RSBI. Other examples of X2.sub.R may
include thoracic impedance magnitude indicating thoracic fluid
accumulation. Examples of the third signal metric X3.sub.R for
cardiac risk assessment may include physical activity intensity, or
a time duration when the physical activity intensity satisfies a
specified condition such as above a threshold.
[0081] The secondary risk generator 420 may generate the secondary
cardiac risk indication (R2) using methods similar to those used by
the secondary detector 244 for generating the secondary detection
indication D2 as previously discussed with reference to FIG.2. For
example, similar to the secondary detector 244 that take as input
at least the second signal metric X2.sub.D and the risk indication
R, the secondary risk generator 420 takes as input at least the
second and third cardiac signal metrics X2.sub.R and X3.sub.R to
generate the secondary cardiac risk indication (R2). In an example,
R2 may be a weighted combination of the second and third cardiac
signal metrics X2.sub.R and X3.sub.R. In an example, R2 may be a
nonlinear combination of X2.sub.R and X3.sub.R, such as the second
signal metric X2.sub.R weighted by the third signal metric
X3.sub.R. In another example, the secondary cardiac risk indication
R2 may be determined using the second signal metric X2.sub.R
measured during a time period when the third signal metric X3.sub.R
satisfies a specified condition. Examples of sampling the second
signal metric X2.sub.R conditional upon the third signal metric
X3.sub.R are discussed below, such as with reference to FIG. 5. The
secondary cardiac risk indication (R2) may be computed as a
statistical measure, such as a central tendency or a variability,
of the linearly or nonlinearly combined X2.sub.R and X3.sub.R, or
from the conditionally sampled X2.sub.R upon X3.sub.R satisfying a
specified condition.
[0082] The blending circuit 430 may combine the primary and
secondary risk indications R1 and R2 to generate a composite
cardiac risk indication (cR), such as according to a fusion model.
A fusion model may include one or more signal metrics and an
algorithm for computing a risk indication from the one or more
signal metrics. Examples of the fusion models may include a linear
weighted combination, a nonlinear combination such as a decision
tree, a neural network, a fuzzy-logic model, or a multivariate
regression model, among others. The blending circuit 430 may
generate the composite cardiac risk indication cR using a first
statistic of a plurality of measurements of the signal metric
X1.sub.R and a second statistic of a plurality of measurements of
the combined metric between X2.sub.R and X3.sub.R. Examples of the
first and second statistics may each include a first-order
statistic such as a central tendency measure or a second-order
statistic such as a variability measure. In an example, the primary
cardiac risk indication R1 includes a central tendency of a
plurality of measurements of the signal metric X1.sub.R, and the
secondary cardiac risk indication R2 includes a variability of a
plurality of measurements of the linearly or nonlinearly combined
metric between X2.sub.R and X3.sub.R or conditionally sampled
X2.sub.R. The blending circuit 430 may generate the composite
cardiac risk indication cR by combining the central tendency of
X1.sub.R and the variability of X2.sub.R or the variability of the
combined X2.sub.R and X3.sub.R. In another example, the blending
circuit 430 may generate the composite cardiac risk indication cR
by combining the central tendency of X1.sub.R and the central
tendency of X2.sub.R or the central tendency of the combined
X2.sub.R and X3.sub.R.
[0083] The risk stratifier circuit 400 may include a transformation
circuit to transform the cR such as to be within a specified range
(e.g., between 0 and 1). The transformation may include a linear
function, a piecewise linear function, or a nonlinear function. By
way of non-limiting example, the transformation circuit may
transform the cR using a sigmoid function, such as provided by
Equation (7):
cR=1/(1+exp (-k*cR+b)) (7)
where "exp" denotes the exponential function, "k" is a positive
number, and "b" is scalar.
[0084] In some examples, the risk stratifier circuit 400 may
include a fusion model selector circuit that may select a fusion
model from a plurality of candidate fusion models, and the blending
circuit 430 may generate the composite cardiac risk indication cR
according to the selected fusion model. The fusion model selection
may be based on signal quality of the one or more physiological
signals from which the cardiac signal metrics X1.sub.R, X2.sub.R,
or X3.sub.R are generated. In an example, between a first candidate
fusion model that employs a respiration signal metric and a second
candidate fusion model that employs a thoracic impedance signal
metric, if the respiration signal has a poor signal-to-noise ratio
(SNR) or excessive variability compared to a specified signal
quality criterion, or substantially out of a specified value range,
then the blending circuit 430 may switch to a the second fusion
model utilizing the thoracic impedance signal metric for combining
the primary and secondary risk indications.
[0085] The optional indication-based risk adjuster 440 may adjust
the cardiac risk indications R1 or R2 according to information
about the patient clinical indications. The clinical indications
may include patient medical history such as historical cardiac
events, heart failure comorbidities or other concomitant disease
states, exacerbation of recent chronic disease, a previous medical
procedure, a clinical lab test result, patient medication intake or
other treatment undertaken, patient physical assessment, or patient
demographics such as age, gender, race, or ethnicity. The clinical
indications may be provided by a clinician such as via the user
interface 260, or stored in a memory such as an electronic medical
record (EMR) system. The blending circuit 430 may generate the
composite cardiac risk indication further using the patient's
clinical information about the patient. In an example, the
composite cardiac risk indication cR may be adjusted by the
clinician such as via the user interface 260 according to the
patient's clinical indications.
[0086] In some examples, the patient clinical indications may have
time-varying effect on the patient risk of developing a future
disease. For example, a more recent disease state or a surgery may
put the patient at higher risk for developing a future worsening
cardiac disease than a more remote historical disease in patient
medical history. To account for the time-varying effect of the
historical medical event, in an example, the indication-based risk
adjuster 440 may produce time-varying weight factors decaying with
time elapsed from a historical medical event, and apply the
time-varying weight factors to at least one of the primary or
secondary risk indications R1 or R2. The time-varying weight factor
may follow a linear, exponential, or other nonlinear decay function
of the time elapsed from a historical medical event. In another
example, the blending circuit 430 may adjust at least one of R1 or
R2 temporarily. For example, the indication-based risk adjuster 440
may be configured to maintain elevated risks of R1 or R2 above a
baseline risk score within a specified timeframe following a
historical medical event, and resume to the baseline risk score
beyond the specified timeframe. The composite risk indication cR
may be used by the secondary detector 244 to generate the second
detection indication D2, as previously discussed with reference to
FIG. 2.
[0087] FIG. 5 illustrates generally an example of a secondary risk
generator 520 for generating a cardiac risk indication based on
conditional sampling of a signal metric. The secondary risk
generator 520, which is an embodiment of the secondary risk
generator 420 of FIG. 4, may include a sampling circuit 522 to
receive a set of measurements of the second cardiac signal metric
222 (X2.sub.R) from the signal processor circuit 220. The secondary
risk generator 520 may include a comparator 524 to compare the
third cardiac signal metric 223 (X3.sub.R) to a specified
threshold. The sampling circuit 522 may sample the measurements of
X2.sub.R when the third signal metric X3.sub.R satisfies a
specified condition. In an example, the second cardiac signal
metric X2.sub.R may include a respiratory rate and the third
cardiac signal metric X3.sub.R may include physical activity
intensity or the duration of the physical activity above a
threshold. The sampling circuit 522 may sample the respiratory rate
measurements during a time period when a high physical activity is
indicated, such as when the physical activity intensity exceeds a
specified threshold. The conditional risk generator 526 may
generate the secondary cardiac risk indication (R2) using a
statistical measure, such as a central tendency or a variability,
of the sampled respiratory rate measurements produced by the
sampling circuit 522.
[0088] FIG. 6 illustrates generally an example of a method 600 for
detecting a worsening cardiac event. The worsening cardiac event
may include events indicative of progression of cardiac condition,
such as a WHF event or a HF decompensation event. The method 600
may be implemented and operate in an ambulatory medical device such
as an implantable or wearable medical device, or in a remote
patient management system. In an example, the method 600 may be
executed by the worsening cardiac event detector 160 or any
embodiment thereof, or by the external system 125.
[0089] The method 600 begins at 610 by sensing first and second
physiological signals from a patient. Examples of the physiological
signals may include electrocardiograph (ECG), an electrogram (EGM),
an intrathoracic impedance signal, an intracardiac impedance
signal, an arterial pressure signal, a pulmonary artery pressure
signal, a RV pressure signal, a LV coronary pressure signal, a
coronary blood temperature signal, a blood oxygen saturation
signal, central venous pH value, a heart sound (HS) signal, a
posture signal, a physical activity signal, or a respiration
signal, among others.
[0090] At 620, at least a first signal metric may be generated from
the first physiological signal and a second signal metric may be
generated from the second physiological signal. The signal metric
may include statistical or morphological parameters extracted from
the sensed physiological signal. Examples of the signal metrics may
include thoracic impedance magnitude, HS metrics such as
intensities of S1, S2, S3, or S4 heart sounds or a relative
intensity such as a ratio between two heart sound components, a
ratio of S3 heart sound intensity to a reference heart sound
intensity, timing of the S1, S2, S3, or S4 heart sound with respect
to a fiducial point such as a P wave, Q wave, or R wave in an ECG,
a respiratory rate, a tidal volume, a RSBI, physical activity
intensity, or a time duration when the activity intensity is within
a specified range or above a specified threshold, systolic blood
pressure, diastolic blood pressure, mean arterial pressure, or the
timing metrics of these pressure measurements with respect to a
fiducial point, among others. A signal metric trend may include
multiple measurements of the signal metric during a specified
period of time. In an example, the signal metric trend may include
a daily trend including daily measurement of a signal metric over a
specified number of days.
[0091] At 630, a cardiac risk indicating a risk of the patient
developing a future worsening cardiac event may be generated from
one or more signal metrics of the physiological signal, such as by
using the risk stratifier circuit 230 as shown in FIG. 2. The
signal metrics for assessing cardiac risk may be different from the
signal metrics for detecting the cardiac event. In an example, the
signal metrics for cardiac risk assessment may include intensity of
a heart sound component such as S3 heart sound measured from a
heart sound signal, a respiratory rate or tidal volume measured
from a respiration signal, thoracic impedance measured from an
impedance signal such as using electrodes on one or more
implantable leads and implantable device can housing, or physical
activity intensity level measured from an physical activity signal
such as using an ambulatory accelerometer associated with the
patient. Examples of generating the cardiac risk using a plurality
of signal metrics are discussed below, such as with reference to
FIG. 9.
[0092] At 640, primary and secondary detection indications may be
generated such as by using the detector circuit 240 as illustrated
in FIG. 2. The primary detection indication D1 may be based on
temporal change of at least the first signal metric from a
reference level representing a signal metric baseline. In an
example, a relative difference between a central tendency of the
first signal metric within a short-term window and a baseline value
determined within a long-term window preceding the short-term
window may be determined, and a worsening cardiac event may be
deemed detected if the relative difference exceeds a specified
threshold. The primary detection indication D1 may have discrete or
continuous values. The secondary detection indication D2 may be
based on a temporal change of at least the second signal metric,
such as a relative difference between a representative value of the
second signal metric within a short-term time window and baseline
value within a long-term time window preceding the short-term time
window in time. As discussed in the examples with reference to
FIGS. 3A-D, the secondary detection indication may be generated
using a linear, nonlinear, or logical combination of the relative
difference and the risk indication.
[0093] At 650, a worsening cardiac event may be detected using the
primary and secondary detection indications. A composite detection
indication (CDI) may be generated using a decision tree that
includes a logical combination of the primary detection indication
D1 and the secondary detection indication D2, such as a Boolean
logic "OR" combination between D1 and D2. The decision tree may
include a sub-decision tree representing a logical combination of
the risk indication (R) and the second signal metric. In an
example, the secondary detection indication D2 is a Boolean logic
"AND" combination between the second signal metric and the risk
indication. In various examples, at least one of the primary or
secondary detection indications may include a Boolean-logic or
fuzzy-logic combination of two or more signal metrics. The risk
indication may similarly include a Boolean-logic or fuzzy-logic
combination of two or more risk indications. Examples of the
decision tree including the primary and secondary detection
indications are discussed below, such as with reference to FIGS.
7A-B.
[0094] At 660, the CDI may be presented to a system user or to a
process such as an alert circuit for producing an alert when the
worsening cardiac event is detected. Additional information that
may be displayed includes physiological signals and the signals
metrics, risk indications, or primary and secondary detection
indications, among others. The information may be presented in a
table, a chart, a diagram, or any other types of textual, tabular,
or graphical presentation formats, for displaying to a system user.
The alert may include audio or other human-perceptible media
format.
[0095] The method 600 may additionally include a step 670 of
delivering a therapy to the patient in response to one or more of
the primary or secondary detection indications or the composite
detection indication. Examples of the therapy may include
electrostimulation therapy delivered to the heart, a nerve tissue,
other target tissues in response to the detection of the target
physiological event, or drug therapy including delivering drug to a
tissue or organ. In some examples, at 670, the primary or secondary
detection indications or the composite detection indication may be
used to modify an existing therapy, such as adjusting a stimulation
parameter or drug dosage.
[0096] FIGS. 7A-B illustrate generally examples of decision trees
750A-B for detecting the worsening cardiac event. The decision
trees 750A-B may be embodiments of the detection of the worsening
cardiac event 650 in FIG. 6. The decision trees 750A-B may be
implemented as a set of circuits to perform logical combinations of
at least the primary and secondary detection indications.
Alternatively, at least a portion of the decision tree may be
implemented in a microprocessor circuit executing a set of
instructions including logical combinations of at least the primary
and secondary detection indications.
[0097] FIG. 7A illustrates an example of a decision tree 750A where
the primary or the secondary detection indication is based on a
Boolean-logic combination of two or more signal metrics. At 751, a
heart sound signal metric of a ratio of S3 to S1 heart sound
intensity (S3/S1) may be compared to a threshold T1 to generate a
primary detection indication D1. If S3/S1 exceeds the threshold T1,
then the worsening cardiac event is deemed detected at 754,
corresponding to D1=1. If at 751 S3/S1 does not exceed the
threshold T1, the primary detection indication D1 does not indicate
a detection of worsening of cardiac event (D1=0), and a secondary
detection indication D2 may be generated based on one or more
second signal metrics at 752A and a risk indication determined at
753A. Steps 752A and 753A form a sub-decision tree for determining
the secondary detection indication D2. The second signal metrics
may be chosen from physiological signals that are more sensitive
and less specific to the worsening cardiac event, such as based on
detection performance of the signal metrics across a cohort of
patients. A more sensitive second signal metric may reduce the
false negative detection of the worsening cardiac event declared by
the first signal metric. In the example illustrated in FIG.7A, the
second signal metric includes a thoracic impedance magnitude (Z) or
an rapid-shallow breathing index (RSBI) as a ratio of a respiratory
rate to a tidal volume measurement, both of which may be less
specific and more sensitive than the S3/S1 in detecting a worsening
cardiac event.
[0098] A Boolean-logic combination of Z and RSBI such as an "OR"
operator may be used at 752A to determine whether the second signal
metric (Z or RSBI) indicates a detection of worsening of heart
failure. If either Z or RSBI exceeds the respective threshold T2 or
T3, a risk indication may be generated at 753A to confirm the
positive detection declared by the second signal metric. The risk
indication at 753A includes a Boolean-logic combination of S3 heart
sound intensity and respiratory rate (RR) variability. If both S3
and RR exceed their respective thresholds T4 and T5, then the
detection of the worsening cardiac event is confirmed at 754, and
the process proceeds to step 660 where an alert may be generated.
However, if neither Z nor RSBI exceeds the respective threshold T2
or T3 at 752A, or if at least one of S3 or RR does not exceed the
respective threshold at 753A, then the secondary detection
indication D2 indicates no detection of the worsening cardiac event
at 755. The process may proceed to step 610 where the physiological
signal sensing and event detection processes continue as
illustrated in FIG. 6.
[0099] FIG. 7B illustrates an example of a decision tree 750B where
the primary or the secondary detection indication is based on a
fuzzy-logic combination of two or more signal metrics. Similar to
the decision tree 750A, the decision tree 750B includes a primary
detection based on S3/S1 at 751 and the positive detection of
worsening cardiac event at 754 if S3/S1 exceeds the threshold T1.
If S3/S1 does not exceed T1, a secondary detection indication D2
may be generated using a sub-decision tree including one or more
second signal metrics at 752B and a risk indication determined at
753B. In the example as illustrated in FIG. 7B, a fuzzy-logic
combination such as "maximum" of Z and RSBI is performed at 752B,
and a fuzzy-logic combination such as "minimum" of S3 and RR, is
performed at 753B. The operator "maximum" corresponds to the
Boolean logic operator "OR" at 752A, and the operator "minimum"
corresponds to the Boolean logic operator "AND" at 753A. In an
example, the two or more signal metrics in 752B (Z and RSBI) or
753B (S3 and RR) may be transformed into respective fuzzified
presentations, and the fuzzy-logic combination at 752B or 753B may
be applied to the fuzzified presentations of the respective signal
metrics. If max(Z, RSBI) exceeds the threshold T6 at 752A, and
min(S3, RR) exceeds the threshold T7 at 753B, then the detection of
the worsening cardiac event is confirmed at 754, and the process
proceeds to step 660 to generate an alert of the detected worsening
cardiac event. However, if max (Z, RSBI) does not exceed the
threshold T6 at 752B, or if min (S3, RR) does not exceed the
threshold T7 at 753B, then the secondary detection indication D2
indicates no detection of the worsening cardiac event at 755; and
the process proceeds to step 610 where the physiological signal
sensing and event detection processes continue as illustrated in
FIG. 6.
[0100] FIG. 8 illustrates generally an example of a portion of a
method 800 for detecting a worsening cardiac event based at least
on the first and second signal metrics. The method 800 may be in
addition to or as an alternative of the steps 640 and 650 for
detecting worsening cardiac event based on the primary and
secondary detection indications. At 810, the first and second
signal metric trends, such as those generated at 620, may be
transformed into a unified scale. In an example, a temporal change
of the first signal metric (such as a relative difference between a
short-term window and a baseline value computed from a long-term
window) may be transformed into a first sequence of transformed
indices within a specified range. A temporal change of the second
signal metric may similarly be transformed into a second sequence
of transformed indices within the same specified range, such that
the transformed first and second signal metric trends may be easily
compared or combined. In an example, the transformation of the
first and second signal metric trend may be based on respective
codebook that maps quantized magnitude of respective signal metric
into numerical indices within a specified range, where a larger
code indicates a higher signal magnitude. In an example, the
transformed indices may be obtained from a transformation of linear
or nonlinear combination of more than one signal metrics.
[0101] At 820, the second signal metric may be modulated by the
cardiac risk indication. In an example, the modulation of the
second signal metric may include a scaled temporal change of the
second signal metric weighted by the risk indication. As
illustrated in FIG. 3A, the risk indication may take discrete
values such as "0" or "1", such as to gate the contribution of
temporal change to the secondary detection indication. The risk
indication R may alternatively take real numbers such as between 0
and 1, and weight the contribution of temporal change to the
secondary detection indication. In another example, the modulation
of the second signal metric may include a sampled temporal change
of the second signal metric when the risk indication satisfies a
specified condition. In an example as illustrated in FIG. 3B, the
second signal metric may include a respiratory rate trend, and the
risk indication may include a physical activity intensity. The
respiratory rate trend may be sampled during a time period when the
physical activity intensity exceeds a specified threshold, and the
secondary detection indication may be determined as a statistical
measure, such as a central tendency or a variability, of the
conditionally sampled RR measurements.
[0102] At 830, a composite signal trend cY may be generated using
the transformed first signal metric Y1 and the second signal metric
Y2 modulated by R. The combination may include a linear or
nonlinear combination, such as shown in Equation (4) as previously
discussed. In an example, the composite signal trend cY is a linear
combination of Y1 and Y2*R. In another example, the composite
signal trend cY is a linear combination of Y1 and
conditionally-sampled Y2 upon R satisfying a specified condition.
The composite signal trend cY may then be compared to a threshold
at 840. If cY exceeds the threshold, then the worsening cardiac
event is deemed detected, and an alert is generated at 660. If cY
does not exceed the threshold, then no worsening cardiac event is
deemed detected, and the process may proceed to step 610 where the
physiological signal sensing and event detection processes continue
as illustrated in FIG. 6. In an example, an alert can be generated
if cY exceeds a first threshold. The alert may sustain until cY
falls below a second threshold indicating a recovery or improvement
of the physiological status.
[0103] FIG. 9 illustrates generally an example of a method 930 for
cardiac risk assessment. The method 930 may be an embodiment of the
step 630 of FIG. 6, and may be implemented in and executed by the
risk stratifier circuit 230 of FIG. 2 or the risk stratifier
circuit 400 of FIG. 4.
[0104] The method 930 begins at 931 by generating a primary risk
indication for cardiac risk assessment from a first signal metric
(X1.sub.R) for cardiac risk assessment. The signal metric X1.sub.R
may be different from the signal metrics used for detecting
worsening cardiac event. In an example, the first signal metric
X1.sub.R may be extracted from a heart sound signal, and include
one of a S3 intensity, or a ratio of a S3 intensity to a reference
heart sound intensity such as one of S1 intensity, S2 intensity, or
heart sound energy during a specified portion of the cardiac cycle.
In an example, the primary cardiac risk indication may include a
statistical measure, such as a central tendency a variability, of
the plurality of the measurements of the signal metric
X1.sub.R.
[0105] At 932, a plurality of measurements of a third signal metric
223 (X3.sub.R) for cardiac risk assessment may be taken. X3.sub.R
may be different from the signal metric X1.sub.R for cardiac risk
assessment. At 933, the X3.sub.R may be compared to a specified
condition (such as a threshold) to control a conditional sampling
of a second signal metric X2.sub.R. If X3.sub.R satisfies the
specified condition, a plurality of measurements of the second
signal metric X2.sub.R may be sampled at 934. In an example, the
second cardiac signal metric X2.sub.R may include a respiratory
rate and the third cardiac signal metric X3.sub.R may include
physical activity intensity or the duration of the physical
activity above a threshold. The respiratory rate measurements may
be sampled during a time period when a high physical activity is
indicated, such as when the physical activity intensity exceeds a
specified threshold. Other examples of the signal metric X2.sub.R
may include a tidal volume, a rapid-shallow breathing index (RSBI)
computed as a ratio of the respiratory rate to the tidal volume, or
a thoracic impedance magnitude indicating thoracic fluid
accumulation, among others. Other examples of X3.sub.R may include
time of day, metabolic state, or heart rate, among others.
[0106] At 935, a secondary cardiac risk indication may be
generated. An example of the secondary cardiac risk indication may
include a statistical measure, such as a central tendency or a
variability, of the sampled respiratory rate measurements.
[0107] At 936, the primary and secondary risk indications R1 and R2
may be combined to generate a composite cardiac risk indication
(cR), such as according to a fusion model. The fusion model may
include one or more signal metrics and an algorithm for
transforming the one or more signal metrics into a risk indication.
Examples of the fusion models may include a linear weighted
combination, a nonlinear combination such as a decision tree, a
neural network, a fuzzy-logic model, or a multivariate regression
model, among others. In an example, a fusion model may be selected
according signal quality of the one or more physiological signals
from which the cardiac signal metrics X1.sub.R, X2.sub.R, or
X3.sub.R are generated. For example, a first candidate fusion model
that employs a physiological signal with a higher signal-to-noise
ratio (SNR) may be selected over a second candidate fusion model
that employs a physiological signal with a lower SNR. The composite
cardiac risk indication cR may be generated by combining a first
statistic of a plurality of measurements of the signal metric
X1.sub.R and a second statistic of a plurality of measurements of
the combined metric between X2.sub.R and X3.sub.R. Examples of the
first and second statistics may each include a first-order
statistic such as a central tendency measure or a second-order
statistic such as a variability measure. In an example, the primary
cardiac risk indication R1 includes a central tendency or other
first-order statistics of a plurality of measurements of the signal
metric X1.sub.R, and the secondary cardiac risk indication R2
includes a variability or other second-order statistics of a
plurality of measurements of the linearly or nonlinearly combined
metric between X2.sub.R and X3.sub.R or conditionally sampled
X2.sub.R. The composite cardiac risk indication cR may be generated
by combining the central tendency of X1.sub.R and the variability
of the X2.sub.R or the variability of the combined X2.sub.R and
X3.sub.R.
[0108] At 937, the cardiac risk indications R1 or R2 may be
adjusted according to information about the patient clinical
indications. The clinical indications may include patient medical
history such as historical cardiac events, heart failure
comorbidities or other concomitant disease states, exacerbation of
recent chronic disease, a previous medical procedure, a clinical
lab test result, patient medication intake or other treatment
undertaken, patient physical assessment, or patient demographics
such as age, gender, race, or ethnicity. In an example, the
composite cardiac risk indication cR may be adjusted by the
clinician. In an example, at least one of the primary or secondary
risk indications R1 or R2 may be weighted by time-varying weight
factors that decay with time elapsed from a historical medical
event may be applied to. The time-varying weight factor may follow
a linear, exponential, or other nonlinear decay function of the
time elapsed from a historical medical event. In another example,
at least one of R1 or R2 may be adjusted temporarily. For example,
an elevated risks of R1 or R2 above a baseline risk score may be
applied within a specified timeframe following a historical medical
event, and resume to the baseline risk score beyond the specified
timeframe. The composite risk indication cR may then be used to
generate the second detection indication at 640.
[0109] The above detailed description includes references to the
accompanying drawings, which form a part of the detailed
description. The drawings show, by way of illustration, specific
embodiments in which the disclosure may be practiced. These
embodiments are also referred to herein as "examples." Such
examples may include elements in addition to those shown or
described. However, the present inventors also contemplate examples
in which only those elements shown or described are provided.
Moreover, the present inventors also contemplate examples using any
combination or permutation of those elements shown or described (or
one or more aspects thereof), either with respect to a particular
example (or one or more aspects thereof), or with respect to other
examples (or one or more aspects thereof) shown or described
herein.
[0110] In the event of inconsistent usages between this document
and any documents so incorporated by reference, the usage in this
document controls.
[0111] In this document, the terms "a" or "an" are used, as is
common in patent documents, to include one or more than one,
independent of any other instances or usages of "at least one" or
"one or more." In this document, the term "or" is used to refer to
a nonexclusive or, such that "A or B" includes "A but not B," "B
but not A," and "A and B," unless otherwise indicated. In this
document, the terms "including" and "in which" are used as the
plain-English equivalents of the respective terms "comprising" and
"wherein." Also, in the following claims, the terms "including" and
"comprising" are open-ended, that is, a system, device, article,
composition, formulation, or process that includes elements in
addition to those listed after such a term in a claim are still
deemed to fall within the scope of that claim. Moreover, in the
following claims, the terms "first," "second," and "third," etc.
are used merely as labels, and are not intended to impose numerical
requirements on their objects.
[0112] Method examples described herein may be machine or
computer-implemented at least in part. Some examples may include a
computer-readable medium or machine-readable medium encoded with
instructions operable to configure an electronic device to perform
methods as described in the above examples. An implementation of
such methods may include code, such as microcode, assembly language
code, a higher-level language code, or the like. Such code may
include computer readable instructions for performing various
methods. The code may form portions of computer program products.
Further, in an example, the code may be tangibly stored on one or
more volatile, non-transitory, or non-volatile tangible
computer-readable media, such as during execution or at other
times. Examples of these tangible computer-readable media may
include, but are not limited to, hard disks, removable magnetic
disks, removable optical disks (e.g., compact disks and digital
video disks), magnetic cassettes, memory cards or sticks, random
access memories (RAMs), read only memories (ROMs), and the
like.
[0113] The above description is intended to be illustrative, and
not restrictive. For example, the above-described examples (or one
or more aspects thereof) may be used in combination with each
other. Other embodiments may be used, such as by one of ordinary
skill in the art upon reviewing the above description. The Abstract
is provided to comply with 37 C.F.R. .sctn. 1.72(b), to allow the
reader to quickly ascertain the nature of the technical disclosure.
It is submitted with the understanding that it will not be used to
interpret or limit the scope or meaning of the claims. Also, in the
above Detailed Description, various features may be grouped
together to streamline the disclosure. This should not be
interpreted as intending that an unclaimed disclosed feature is
essential to any claim. Rather, inventive subject matter may lie in
less than all features of a particular disclosed embodiment. Thus,
the following claims are hereby incorporated into the Detailed
Description as examples or embodiments, with each claim standing on
its own as a separate embodiment, and it is contemplated that such
embodiments may be combined with each other in various combinations
or permutations. The scope of the disclosure should be determined
with reference to the appended claims, along with the full scope of
equivalents to which such claims are entitled.
* * * * *